Overview

Dataset statistics

Number of variables11
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory278.3 KiB
Average record size in memory96.0 B

Variable types

Numeric11

Alerts

gross_revenue is highly overall correlated with invoice_no and 2 other fieldsHigh correlation
recency_days is highly overall correlated with invoice_noHigh correlation
invoice_no is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
quantity is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
avg_unique_basket_size is highly overall correlated with avg_ticketHigh correlation
avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
quantity is highly skewed (γ1 = 28.46941323)Skewed
avg_ticket is highly skewed (γ1 = 53.44422362)Skewed
returns is highly skewed (γ1 = 51.79774426)Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2023-01-03 20:16:00.464499
Analysis finished2023-01-03 20:16:18.783217
Duration18.32 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:18.900711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2023-01-03T17:16:19.064273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.3217
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:19.225469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.623
Coefficient of variation (CV)3.8484486
Kurtosis353.94472
Mean2749.3217
Median Absolute Deviation (MAD)672.16
Skewness16.777556
Sum8162736.2
Variance1.1194959 × 108
MonotonicityNot monotonic
2023-01-03T17:16:19.372455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.287639
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:19.535589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756779
Coefficient of variation (CV)1.2095137
Kurtosis2.7779627
Mean64.287639
Median Absolute Deviation (MAD)26
Skewness1.7983795
Sum190870
Variance6046.1167
MonotonicityNot monotonic
2023-01-03T17:16:19.687923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

invoice_no
Real number (ℝ)

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7231391
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:19.855242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8565313
Coefficient of variation (CV)1.5474954
Kurtosis190.83445
Mean5.7231391
Median Absolute Deviation (MAD)2
Skewness10.766805
Sum16992
Variance78.438147
MonotonicityNot monotonic
2023-01-03T17:16:20.000874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

quantity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct747
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean384.94038
Minimum1
Maximum80996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:20.148789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q185
median154
Q3293
95-th percentile932.2
Maximum80996
Range80995
Interquartile range (IQR)208

Descriptive statistics

Standard deviation1941.885
Coefficient of variation (CV)5.0446383
Kurtosis1064.3611
Mean384.94038
Median Absolute Deviation (MAD)86
Skewness28.469413
Sum1142888
Variance3770917.4
MonotonicityNot monotonic
2023-01-03T17:16:20.317924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 28
 
0.9%
70 26
 
0.9%
67 24
 
0.8%
66 23
 
0.8%
90 22
 
0.7%
120 22
 
0.7%
52 21
 
0.7%
69 19
 
0.6%
75 19
 
0.6%
84 19
 
0.6%
Other values (737) 2746
92.5%
ValueCountFrequency (%)
1 3
0.1%
3 2
 
0.1%
6 2
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 4
0.1%
11 1
 
< 0.1%
12 6
0.2%
13 1
 
< 0.1%
15 2
 
0.1%
ValueCountFrequency (%)
80996 1
< 0.1%
38639 1
< 0.1%
21352 1
< 0.1%
17376 1
< 0.1%
17150 1
< 0.1%
16288 1
< 0.1%
15837 1
< 0.1%
13369 1
< 0.1%
12872 1
< 0.1%
10827 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.897762
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:20.481975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.956587
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9344
Coefficient of variation (CV)19.98033
Kurtosis2890.7071
Mean51.897762
Median Absolute Deviation (MAD)5.984842
Skewness53.444224
Sum154084.45
Variance1075233
MonotonicityNot monotonic
2023-01-03T17:16:20.629082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:21.035454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2023-01-03T17:16:21.203649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_recency_days
Real number (ℝ)

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.348511
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:21.382540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.923077
median48.285714
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.410256

Descriptive statistics

Standard deviation63.544929
Coefficient of variation (CV)0.94352388
Kurtosis4.8871091
Mean67.348511
Median Absolute Deviation (MAD)26.285714
Skewness2.0627709
Sum199957.73
Variance4037.958
MonotonicityNot monotonic
2023-01-03T17:16:21.538545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
46 17
 
0.6%
21 17
 
0.6%
11 17
 
0.6%
42 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

Distinct1350
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063278078
Minimum0.0054495913
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:21.703243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0094339623
Q10.017777778
median0.029411765
Q30.055401662
95-th percentile0.22222222
Maximum3
Range2.9945504
Interquartile range (IQR)0.037623884

Descriptive statistics

Standard deviation0.13448206
Coefficient of variation (CV)2.1252552
Kurtosis121.55755
Mean0.063278078
Median Absolute Deviation (MAD)0.014338235
Skewness8.7732594
Sum187.87261
Variance0.018085426
MonotonicityNot monotonic
2023-01-03T17:16:21.861354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1666666667 21
 
0.7%
0.3333333333 21
 
0.7%
0.02777777778 20
 
0.7%
0.09090909091 19
 
0.6%
0.0625 17
 
0.6%
0.1333333333 16
 
0.5%
0.4 16
 
0.5%
0.25 15
 
0.5%
0.02380952381 15
 
0.5%
0.03571428571 15
 
0.5%
Other values (1340) 2794
94.1%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005509641873 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
2 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.5 3
 
0.1%
1 14
0.5%
0.8333333333 1
 
< 0.1%
0.75 1
 
< 0.1%
0.6666666667 12
0.4%
0.6514745308 1
 
< 0.1%
0.6 1
 
< 0.1%

avg_unique_basket_size
Real number (ℝ)

Distinct1005
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.154708
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:22.021832image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.512322
Coefficient of variation (CV)0.88073027
Kurtosis27.703297
Mean22.154708
Median Absolute Deviation (MAD)8.2
Skewness3.4994559
Sum65777.329
Variance380.73071
MonotonicityNot monotonic
2023-01-03T17:16:22.192090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 53
 
1.8%
14 39
 
1.3%
11 38
 
1.3%
20 33
 
1.1%
9 33
 
1.1%
1 32
 
1.1%
17 31
 
1.0%
18 30
 
1.0%
10 30
 
1.0%
5 29
 
1.0%
Other values (995) 2621
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.81376
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2023-01-03T17:16:22.360178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.33333
Q3281.69231
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.44231

Descriptive statistics

Standard deviation791.55519
Coefficient of variation (CV)3.1685812
Kurtosis2255.5382
Mean249.81376
Median Absolute Deviation (MAD)83.083333
Skewness44.672717
Sum741697.07
Variance626559.62
MonotonicityNot monotonic
2023-01-03T17:16:22.518891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
60 8
 
0.3%
88 8
 
0.3%
75 8
 
0.3%
136 8
 
0.3%
208 7
 
0.2%
Other values (1969) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

Interactions

2023-01-03T17:16:16.790655image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:00.796683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:02.307366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:03.894459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:05.516720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:07.007586image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:08.793323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:10.260160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:11.877706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:13.491021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:15.278236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:16.937802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:00.939422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:02.430644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:04.039795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:05.647919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:07.148279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:08.933149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:10.406140image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:12.006607image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:13.620430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:15.405910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:17.085726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:01.067202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:02.557465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:04.172835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:05.772479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:07.280889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:09.066901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:10.544958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:12.141907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:13.749855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:15.532042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:17.231011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:01.201783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:02.690816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:04.317635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:05.905232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:07.427743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:09.200845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:10.688072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:12.285303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:13.896454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:15.668989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:17.366461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:01.317489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:02.810995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:04.458100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:06.030267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:07.558972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:09.319984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:10.822119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:12.416275image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:14.023101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:15.798473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:17.530605image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:01.452704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:02.957446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:04.613910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:06.175010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:07.701445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:09.460341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:10.974911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:12.590244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:14.181959image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:15.941725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:17.668364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:01.583977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:03.085587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:04.757270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:06.302982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:07.843726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:09.588571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:11.106310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:12.728981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:14.536436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:16.085202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:17.820866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:01.740079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:03.222964image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:04.919138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:06.449203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:08.015029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:09.723281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:11.261156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:12.889944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:14.683128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:16.244841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:17.959411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:01.885794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:03.357875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:05.060482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:06.589363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:08.169920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:09.857689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:11.413587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:13.048177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:14.842394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:16.379562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:18.100991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:02.022030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:03.488193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:05.218737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:06.723151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:08.328789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:09.988889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:11.565217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:13.195447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:14.991965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:16.513880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:18.238066image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:02.154912image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:03.746786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:05.362801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:06.850975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:08.635691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:10.115821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:11.718101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:13.338913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:15.132826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-03T17:16:16.643457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-03T17:16:22.667958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
customer_idgross_revenuerecency_daysinvoice_noquantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
customer_id1.000-0.0760.0010.026-0.043-0.131-0.0630.019-0.008-0.007-0.123
gross_revenue-0.0761.000-0.4150.7700.7080.2460.372-0.2470.1610.2910.574
recency_days0.001-0.4151.000-0.502-0.2900.048-0.1200.108-0.031-0.106-0.098
invoice_no0.0260.770-0.5021.0000.5280.0590.294-0.2590.1490.0250.100
quantity-0.0430.708-0.2900.5281.0000.3440.257-0.1670.1060.0620.671
avg_ticket-0.1310.2460.0480.0590.3441.0000.190-0.1220.098-0.6110.188
returns-0.0630.372-0.1200.2940.2570.1901.000-0.3960.3590.0190.210
avg_recency_days0.019-0.2470.108-0.259-0.167-0.122-0.3961.000-0.9620.048-0.077
frequency-0.0080.161-0.0310.1490.1060.0980.359-0.9621.000-0.0420.057
avg_unique_basket_size-0.0070.291-0.1060.0250.062-0.6110.0190.048-0.0421.0000.447
avg_basket_size-0.1230.574-0.0980.1000.6710.1880.210-0.0770.0570.4471.000

Missing values

2023-01-03T17:16:18.447477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-03T17:16:18.676485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysinvoice_noquantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
0178505391.21372.034.035.018.15222240.035.5000000.4861118.73529450.970588
1130473232.5956.09.0131.018.90403535.027.2500000.04878019.000000154.444444
2125836705.382.015.01568.028.90250050.023.1875000.04569915.466667335.200000
313748948.2595.05.0169.033.8660710.092.6666670.0179215.60000087.800000
415100876.00333.03.048.0292.00000022.08.6000000.1363641.00000026.666667
5152914623.3025.014.0508.045.32647129.023.2000000.0544417.285714150.142857
6146885630.877.021.0579.017.219786399.018.3000000.07356915.571429172.428571
7178095411.9116.012.0961.088.71983641.035.7000000.0391065.083333171.416667
81531160767.900.091.02167.025.543464474.04.1444440.31550826.142857419.714286
9160982005.6387.07.0240.029.9347760.047.6666670.0243909.57142987.571429
customer_idgross_revenuerecency_daysinvoice_noquantityavg_ticketreturnsavg_recency_daysfrequencyavg_unique_basket_sizeavg_basket_size
5627177271060.2515.01.0111.016.0643946.06.00.28571466.0645.000000
563717232421.522.02.066.011.7088890.012.00.15384618.0101.500000
563817468137.0010.02.044.027.4000000.04.00.4000002.558.000000
564913596697.045.02.081.04.1990360.07.00.25000083.0203.000000
5655148931237.859.02.0226.016.9568490.02.00.66666736.5399.500000
565912479473.2011.01.087.015.77333334.04.00.33333330.0382.000000
568014126706.137.03.0361.047.07533350.03.01.0000005.0169.333333
5686135211092.391.03.046.02.5112410.04.50.300000145.0244.333333
569615060301.848.04.057.02.5153330.01.02.00000030.065.500000
571512558269.967.01.0102.024.541818196.06.00.28571411.0196.000000